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Generative AI Developer

A Generative AI Developer builds applications powered by generative models for text, images, code, or audio. They design retrieval and prompting strategies, connect models to tools and data, and ship interactive experiences such as chatbots, copilots, and content engines. The role focuses on turning generative capability into useful, controllable, production-ready features.

Responsibilities

Build generative features such as chat, copilots, and content tools
Implement RAG pipelines and tool or function calling
Design prompts and structured outputs for reliability
Integrate models with product data and external APIs
Add guardrails, evaluation, and cost controls
Iterate on quality using real usage feedback

Must-have skills

Strong application development skills (Python, JavaScript/TypeScript)
Hands-on experience with LLM and generative APIs
Retrieval-augmented generation and embeddings
Prompt design and structured output handling
API integration and backend fundamentals

Nice-to-have skills

Vector database experience
Agent frameworks and orchestration
Fine-tuning or adapters
Streaming UIs for generative output

Average Salary

Typical US base salary for an HR Generalist by experience level.

Junior

0–2 yrs experience

$110,000 – $140,000
US annual salary (USD)

Intermediate

0–2 yrs experience

$140,000 – $180,000

US annual salary (USD)

Senior

0–2 yrs experience

$180,000 – $235,000

US annual salary (USD)

Figures are annual US market estimates for orientation, not offers. Actual pay varies by location, company stage, and equity.

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Frequently asked questions

What does a Generative AI Developer build?
Chatbots, copilots, content generation tools, code assistants, and other features that create text, images, or audio, wired into real product data and workflows.
Generative AI Developer vs AI Engineer: are they the same?
They overlap. Generative AI Developer emphasizes building generative applications specifically, while AI Engineer is a broader term covering many kinds of AI integration.
What is RAG and why does it matter here?
Retrieval-augmented generation feeds relevant data into a model at query time, improving accuracy and grounding. It is a core technique for generative apps.
Do I need to train models for this role?
Usually not. Most work uses existing foundation models through APIs, with prompting, retrieval, and occasional fine-tuning rather than training from scratch.

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